Diagnostic performance of endoscopic ultrasound‐artificial intelligence using deep learning analysis of gallbladder polypoid lesions

2021 ◽  
Author(s):  
Sung Ill Jang ◽  
Young Jae Kim ◽  
Eui Joo Kim ◽  
Huapyong Kang ◽  
Seung Jin Shon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Endoscopic Ultrasound ◽  
Diagnostic Performance ◽  
Learning Analysis

scholarly journals Diagnostic Performance of Endoscopic Ultrasound-Artificial Intelligence Using Deep Learning Analysis of Gallbladder Polypoid Lesions: A Feasibility Study

2021 ◽  
Author(s):  
Sung Ill Jang ◽  
Young Jae Kim ◽  
Eui Joo Kim ◽  
Huapyong Kang ◽  
Seung Jin Shon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Endoscopic Ultrasound ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Validation Cohort ◽  
External Validation ◽  
Diagnostic Modality ◽  
Internal Validation

Abstract Endoscopic ultrasound (EUS) is the most accurate diagnostic modality for polypoid lesions of the gallbladder (GB), but is limited by subjective interpretation. We evaluated the diagnostic performance of deep learning-based artificial intelligence (AI) in differentiating polypoid lesions using EUS images. The diagnostic performance of the EUS-AI system with ResNet50 architecture was evaluated via three processes: training, internal validation, and testing. The diagnostic performance was also verified using an external validation cohort and compared with the performance of EUS endoscopists. In the AI development cohort, the diagnostic performance of EUS-AI including sensitivity, specificity, positive predictive value, negative predictive value and accuracy. For the differential diagnosis of neoplastic and non-neoplastic GB polyps, these values for EUS-AI were 77.8%, 91.6%, 57.9%, 96.5%, and 89.8%, respectively. In the external validation cohort, the differential diagnosis of neoplastic and non-neoplastic GB polyps, these values were 60.3%, 77.4%, 36.2%, 90.2%, and 74.4%, respectively, for EUS-AI; they were 74.2%, 44.9%, 75.4%, 46.2%, and 65.3%, respectively, for the endoscopists. The accuracy of the EUS-AI was between the accuracies of mid-level (66.7%) and expert EUS endoscopists (77.5%). This EUS-AI system showed favorable performance for the diagnosis of neoplastic GB polyps, with a performance comparable to that of EUS endoscopists.

Deep learning-based artificial intelligence applications in prostate MRI: brief summary

2021 ◽  
Author(s):  
Baris Turkbey ◽  
Masoom A. Haider
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Diagnostic Performance ◽  
Lesion Detection ◽  
Western World ◽  
Complex Nature ◽  
Cancer Type ◽  
Prostate Mri ◽  
Prostate Segmentation ◽  
Established Role

Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI.

28525 Artificial intelligence and deep learning analysis of half a million individuals with European origin reveals specific skin aging process

2021 ◽  
Vol 85 (3) ◽  
pp. AB195
Author(s):  
Frederic Flament ◽  
Laurie Jacquet ◽  
Marion Nielsen ◽  
Delphine Kerob ◽  
Ruowei Jiang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Aging Process ◽  
Skin Aging ◽  
European Origin ◽  
Learning Analysis

scholarly journals Preliminary Study on the Diagnostic Performance of a Deep Learning System for Submandibular Gland Inflammation Using Ultrasonography Images

2021 ◽  
Vol 10 (19) ◽  
pp. 4508
Author(s):  
Yoshitaka Kise ◽  
Chiaki Kuwada ◽  
Yoshiko Ariji ◽  
Munetaka Naitoh ◽  
Eiichiro Ariji
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Test Group ◽  
Training Group ◽  
Learning System ◽  
Control Group ◽  
Preliminary Study ◽  
And Control ◽  
Learning Analysis ◽  
Total Accuracy

This study was performed to evaluate the diagnostic performance of deep learning systems using ultrasonography (USG) images of the submandibular glands (SMGs) in three different conditions: obstructive sialoadenitis, Sjögren’s syndrome (SjS), and normal glands. Fifty USG images with a confirmed diagnosis of obstructive sialoadenitis, 50 USG images with a confirmed diagnosis of SjS, and 50 USG images with no SMG abnormalities were included in the study. The training group comprised 40 obstructive sialoadenitis images, 40 SjS images, and 40 control images, and the test group comprised 10 obstructive sialoadenitis images, 10 SjS images, and 10 control images for deep learning analysis. The performance of the deep learning system was calculated and compared between two experienced radiologists. The sensitivity of the deep learning system in the obstructive sialoadenitis group, SjS group, and control group was 55.0%, 83.0%, and 73.0%, respectively, and the total accuracy was 70.3%. The sensitivity of the two radiologists was 64.0%, 72.0%, and 86.0%, respectively, and the total accuracy was 74.0%. This study revealed that the deep learning system was more sensitive than experienced radiologists in diagnosing SjS in USG images of two case groups and a group of healthy subjects in inflammation of SMGs.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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